3489
The Journal of Experimental Biology 209, 3489-3498
Published by The Company of Biologists 2006
doi:10.1242/jeb.02385
Energetic influence on gull flight strategy selection
Judy Shamoun-Baranes* and Emiel van Loon
Computational Bio- and Physical Geography, Institute for Biodiversity and Ecosystem Dynamics, University of
Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands
*Author for correspondence (e-mail: [email protected])
Accepted 15 June 2006
Summary
During non-migratory flight, gulls (Larids) use a wide
flapping. Hence the ratio of flapping to soaring may be
variety of flight strategies. We investigate the extent to
higher than for other air and ground speed combinations.
which the energy balance of a bird explains flight strategy
This range of speeds is broadest for black-headed gulls.
selection. We develop a model based on optimal foraging
The model results are supported by the observations. For
and aerodynamic theories, to calculate the ground speeds
example, flapping is more prevalent at speeds where the
and airspeeds at which a gull is expected to flap or soar
predicted net energy gain is similar for both strategies.
during foraging flight. The model results are compared
Interestingly, combinations of air speed and flight speed
with observed flight speeds, directions, and flight strategies
that, according to the model, would result in a loss of net
of two species of gulls, the black-headed gull Larus
energy gain, were not observed. Additional factors that
ridibundus and the lesser black-backed gull Larus fuscus.
may influence flight strategy selection are also briefly
The observations were made using a tracking radar over
discussed.
land in The Netherlands.
The model suggests that, especially at combinations
Supplementary material available online at
http://jeb.biologists.org/cgi/content/full/209/18/3489/DC1
of low ground speed (~5–10·m·s–1), high air speed
(~20–25·m·s–1) and low ground and air speed, gulls should
favor soaring flight. At intermediate ground and air speeds
Key words: flapping flight, foraging theory, Larus ridibundus, Larus
fuscus, soaring.
the predicted net energy gain is similar for soaring and
Introduction
Aerodynamic theory adapted for avian flight (e.g. Tucker,
1975; Pennycuick, 1989; Rayner, 2001) is a tool that has been
used to develop and investigate theories in optimal flight
behavior of birds (Alerstam and Lindstrom, 1990; Hedenström
and Alerstam, 1995; Liechti and Bruderer, 1998; Thomas and
Hedenström, 1998; Hedenström, 2002). Some birds may
specialize in either powered flapping flight or soaring flight,
whereas other birds such as many Larids (gulls), the focus of
this study, utilize a wide variety of flight strategies (e.g. Snow
and Perrins, 1998). Theoretically, powered flight is
energetically more expensive for many species than soaring
flight (Hedenström, 1993). Field measurements, for example,
have also shown that the energetic cost of soaring by herring
gulls (Larus argentatus) is much lower than for flapping
(Kanwisher et al., 1978). However, for species that can use
different flight strategies, the reasons for selecting a particular
flight strategy and the factors determining the flight strategy
used remain unclear and have received little attention.
Foraging is an interesting case for studying flight strategy
selection because the selection of a particular foraging behavior
may strongly influence energy expenditure (Bautista et al.,
2001; Weimerskirch et al., 2003). Different currencies in
optimal foraging theory can be used to develop and test
expectations for foraging behavior of birds (Welham and
Ydenberg, 1993; Hedenström and Alerstam, 1995; Bautista et
al., 1998). Energy balance, meteorological conditions, or a
combination of the two, may be some of the factors that
influence flight strategy selection during foraging (Woodcock,
1975; Bautista et al., 2001; Sergio, 2003; Ruxton and Houston,
2004).
In this study we investigate to what extent net energy balances
of birds can explain the selection of a soaring or a flapping flight
strategy. We develop a static model for flight behavior based on
a theoretical framework encompassing optimal foraging and
aerodynamic theories. The main hypothesis underlying our
model is that the net energy balance over a short time period for
an individual bird determines largely whether a bird chooses
flapping or soaring flight when foraging. Since flight energetics
vary greatly with bird morphology (e.g. Pennycuick, 1989;
Norberg, 1990), the model is tested for two gull species of
different mass, wing size and shape: the black-headed gull
THE JOURNAL OF EXPERIMENTAL BIOLOGY
3490 J. Shamoun-Baranes and E. van Loon
(Larus ridibundus L.) and the larger lesser black-backed gull
(Larus fuscus L.). The model is compared to field observations
where gull flight behavior has been observed along with
measured physical flight parameters. In this study we evaluate
whether the results predicted by our model are consistent with
the observations. Flight energetics may be strongly influenced
by weather conditions. Therefore a model involving variable
weather conditions would perhaps be more appropriate to study
the proposed system. However, we do not have observations of
bird flight nor weather data at a spatio-temporal resolution to
calibrate or validate a model of that complexity.
Materials and methods
Observations
As part of a larger study on the influence of meteorological
conditions on the flight altitudes of birds (Shamoun-Baranes et
al., 2006), the flight speed, direction, altitude and climb rate of
black-headed gulls Larus ridibundus L. and the larger lesser
black-backed gull Larus fuscus L. were measured.
Measurements were conducted during 15 days in the spring and
summer of 2000 using a modified HSA MLU-flycatcher
tracking radar (Hollandse Signaal Apparaten, Hengelo, The
Netherlands) stationed at De Peel military airbase (51°32⬘N,
5°52⬘E) in the southeastern region of The Netherlands. The
landscape in the area of measurement is flat terrain including
low forest and heath. The gulls were tracked within a range of
5·km. Birds were identified visually using a video camera with
a 300·mm lens mounted parallel to the tracking radar, as well
as with digital wing beat pattern recognition. The flight strategy
(flapping, soaring or gliding) was recorded during each track.
During observations, soaring and gliding were defined as nonflapping flight with either an increase or decrease in altitude,
respectively. In all subsequent analyses, soaring and gliding are
treated synonymously and measurements were combined and
compared to flapping flight measurements. In total, 54 blackheaded gull flight tracks and 97 lesser black-backed gull tracks
were recorded. As birds were selected randomly in the course
of each day during the 15 days of measurements, we consider
each of these tracks to be independent measurements of unique
individuals. Mean track duration (± s.d.) was 32±15·s. The
mean flight speed (ground speed) was calculated per track and
used in the analyses. All observations are of local nonmigratory movements. For the purpose of this study, although
we do not know the exact aim of the flights of the gulls tracked,
we make the assumption (based on time of year and time of
day) that gulls are moving to and/or from foraging sites. Tracks
of birds that were clearly foraging on aerial prey were excluded
from analysis.
Hourly surface wind speed and direction data were collected
from the nearest meteorological station at Volkel (51°39⬘N,
5°42⬘E). For comparison with optimal foraging predictions we
calculated flight air speed and direction from tracked ground
speed and direction by using vector summation and subtracting
the wind vectors from the flight vectors. The wind speed and
direction at the same time and location (horizontal and vertical)
of the flight measurements would be optimal; however, they
were unavailable. Although the meteorological station is
approximately 17·km from the radar location, the surface winds
in both areas are comparable considering the landscape
properties of the measurement area and the meteorological
station (Wieringa, 1986). Furthermore, due to intense vertical
mixing in the mixed boundary layer, corresponding to the
altitudes at which birds were observed, wind speed and
direction are virtually constant over most of the mixed layer
(Stull, 1988). Using 12 GMT radiosonde data from De Bilt
(52°06⬘N, 5°11⬘E), we tested the relationship between winds
at 2·m and winds at the 925·mb (1·mb=0.01·Pa) pressure level
(approximately 650–850·m) by applying a linear regression
analysis of the u component of the wind at 2·m in relation to
the u component of wind at 925·mb pressure level. The same
analysis was repeated for the v component of wind. R2 values
for u and v components were 0.83 and 0.87 for the u and v
components respectively (P<0.001). Therefore, the surface
winds measured at Volkel should be a reasonable estimation of
the winds aloft, experienced by the birds. Nevertheless,
remotely measured wind that may differ from the wind
experienced by the bird will add some uncertainty to the air
speed calculations of the gulls.
Predictions from optimal foraging and aerodynamic theory
One of the fitness-related currencies that may be maximized
in optimal foraging theory is the net rate of energy gain
(Bautista et al., 1998). In a laboratory experiment (Bautista et
al., 2001), the switch between walking or flying modes of
foraging starlings (Sturnus vulgaris) showed that net rate of
energy gain was the currency that best accounted for the choice
of foraging mode. Therefore we use the same currency in our
study.
All symbols used in the following equations are summarized
in Tables 1 and 2. Similar to calculations by other authors
(Hedenström and Alerstam, 1995), we calculate the net rate of
energy gain when flying between foraging patches as follows:
R=
⎞
1 ⎛ ⌠ tp
⌠ tt
⎜ ⎮ En(t)dt – ⎮ P(t)dt⎟ ,
tp + tt ⎝ ⌡0
⎠
⌡0
(1)
where R is net rate of energy gain (W), En(t) is the energy gain
function during foraging (W), P(t) is the associated power of
flight (W), tp is the time of feeding on a patch (s), and tt is travel
time between patches (s).
In optimal foraging studies, energy gain is usually assumed
to be a non-linear function relative to the time spent feeding
(e.g. Charnov, 1976; Tome, 1988; McNamara and Houston,
1997). However, little is known about the precise shape of En(t)
for a given species, the results for the ruddy duck (Oxyura
jamaicensis) (Tome, 1988) and ring-billed gull (Larus
delawarensis) (Welham and Ydenberg, 1988) being notable
exceptions. In our study, there is little reason to adopt a
complex form for the net energy gain function since we
compare the net energy gain for a single bird species when
flapping or soaring. Only tt and P(t) in Eqn·1 affect net energy
THE JOURNAL OF EXPERIMENTAL BIOLOGY
Energetics and flight strategy selection 3491
Table·1. List of abbreviations and their respective descriptions
and units
Symbol
BMR
c
D
En
P
Pf
Pind
Ppar
Ppro
Ps
R
Rf
Rs
tp
tt
Va
Vg
Vmp
Vmr
Description
Unit
Basal metabolic rate
Constant multiple of BMR for soaring flight
Travel distance
Foraging energy gain function
Power for flight
Power for flapping flight
Induced power
Parasite power
Profile power
Power for gliding/soaring flight
Net rate of energy gain
R for flapping flight
R for soaring flight
Patch time (time for feeding)
Travel time between patches
Air speed
Ground speed
Minimum power air speed
Maximum range air speed
W
–
m
W
W
W
W
W
W
W
W
s
s
m s–1
m s–1
m·s–1
m·s–1
gain of a species (tp and En do not make a difference).
Moreover, we adopt a value for tp that can, within the range of
observed values, be adjusted so that a range of values can be
obtained for the integral
⌠ tp
⎮ En(t)dt .
⌡0
We can therefore simplify our analysis without loss of
generality by replacing En(t) with a constant En, so that
⌠ tp
⎮ En(t)dt
⌡0
simplifies to Entp.
Power of flight P(t) is a nonlinear function that depends on
a bird’s flight strategy, a number of biometric parameters and
wind conditions. Although wind conditions can vary in space
and time we assume P(t) to be constant for a particular flight
between two patches. The most important reason for this
simplification is that we consider the travel time and distance
between patches to be relatively short in relation to the
heterogeneity of the wind field. In addition, we are only able
to observe flight behavior (height, speed, direction, and
flapping or soaring flight) over a very limited part of a flight
track – hence it is practically impossible to define the full power
for flight between two food patches. Hence
⌠ tt
⎮ P(t)dt
⌡0
simplifies to Ptt. When assuming P to be constant over a flight
track, we also assume a constant ground speed for the bird.
Table·2. Biometric parameters and aerodynamic flight
performance predictions for black-headed gull and lesser
black-backed gull
Black-headed gull
Lesser
black-backed gull
0.285
0.967
0.0992
9.43
1.53
1.1
19.06
0.0004
0.77
1.43
0.243
8.52
3.14
1.1
5.7
0.0002
Mass (kg)
Wing span (m)
Wing area (m2)
Aspect ratio
BMR (W)
␣ (Eqn·3)
␤ (Eqn·3)
␥ (Eqn·3)
Values for mass, wing span and wing area for the lesser blackbacked gull are taken from Bruderer and Boldt (Bruderer and Boldt,
2001). Other variables are derived (for calculations, see Appendix in
supplementary material).
Combining this with a fixed distance between food patches,
travel time (tt) can be calculated by D/Vg, where D is the
distance (m) between food patches and Vg (m·s–1) is ground
speed of the bird.
Eqn·1 can now be rewritten as:
R=
Entp
tp + tt
–
Ptt
tp + tt
.
(2)
For a schematic representation of Eqn·1 and 2, see Fig.·1. In our
study we will keep En, tp and D constant, while varying P and Vg.
Based on aerodynamic theory for avian flight, as formulated
by Pennycuick (Pennycuick, 1989), we parameterize P as a
function of air speed and/or basal metabolic rate, depending on
the flight strategy used. The mechanical power for flapping
flight Pf is a function of air speed (Va) and the summation of
profile power (Ppro), parasite power (Ppar) and induced power
(Pind), Eqn·3:
Pf = Ppro + Ppar + Pind = ␣ +
␤
+ ␥ · Va3 ,
Va
(3)
(see Appendix in supplementary material for the full
formulation and all constants included in Eqn·3).
Profile power is the power needed to overcome the drag of
the wings during flight, parasitic power is the power needed to
overcome body drag, and induced power is the power needed
to support the weight of the bird during flight. For calculations
of net rate of energy gain, Pf is converted to chemical power,
the rate of fuel energy consumption, by assuming a conversion
efficiency of 0.23. The body drag coefficient, one of the
constants used to calculate Ppar, is set to 0.1 (Pennycuick et al.,
1996). The power of soaring flight Ps is a constant multiple (c)
of the basal metabolic rate (BMR, in W) and is independent of
speed (see Eqn·4):
THE JOURNAL OF EXPERIMENTAL BIOLOGY
Ps = c · BMR .
(4)
3492 J. Shamoun-Baranes and E. van Loon
En (W)
A
tt(2)
tt(1)
tp(2)
tp(3)
B
En (W)
−P (W)
Time
tp(1)
tt(2)
tt(1)
calculate net rate of energy gain: En=20·W, D=10000·m,
tp=1800·s. Similar values for travel distance, D (Horton et al.,
1983; Gorke and Brandl, 1986; Prevot-Julliard and Lebreton,
1999; Baxter et al., 2003), foraging time tp and flight duration
tt (Morris and Black, 1980; Gorke and Brandl, 1986) have been
reported in field studies for different species of gulls. At a given
combination of ground and air speed the model calculates the
rate of net energy gain for both flapping and soaring flight. We
focus on the relative difference between net energy gain for
flapping and soaring flight to explain flight behavior rather than
the absolute values for net energy gain. Reasons for this are the
uncertainties in the energy gain function En and tp as well as in
the calculation of P (see also Discussion).
−P (W)
Time
tp(1)
tp(2)
tp(3)
Fig.·1. Schematic representation of Eqn·1 and 2, showing our
conceptualization of a bird’s energy balance. Birds gain energy during
foraging and lose energy when traveling between food patches. The
switch between these modes is instantaneous. (A) The general case
where energy gain, En(tp), and the power required for flight, P(tt), are
functions of time (Eqn·1). (B) The simplification used in this study
where En and P are constant over time (Eqn·2). The total travel time is
in both cases calculated by the sum tt=⌺itt(i) and the time spent foraging
is calculated by tp=⌺itp(i). Note that P is given in positive values in
Eqn·1 and 2, so that –P is used on the negative part of the vertical axis.
As suggested (Hedenström, 1993), c is conservatively set to 3.
Values may be even lower, as found for certain sea birds
(Weimerskirch et al., 2000; Weimerskirch et al., 2003). The
cost of gliding flight of the herring gull, for example, was
calculated as approximately 2.4 times the resting metabolic rate
(Baudinette and Schmidt-Nielsen, 1974). Note that analogous
to the subscripts for P, Rf and Rs refer to net rate of energy gain
for flapping and soaring flight respectively. The biometric
parameters required in Eqn·3 and 4 for the black-headed gull
and lesser black-backed gull are specified in Table·2. All
aerodynamic calculations and data analyses were performed in
MATLAB 6.5.
Our expression of R is comparable to other studies where R
is expressed as the difference between the gross rate of energy
gain (the first term in Eqn·1) and the cost or energy expenditure
(the second term in Eqn·1) (e.g. Hedenström and Alerstam,
1995; Bautista et al., 1998; Bautista et al., 2001). One of the
central assumptions about the net rate of energy gain is the
decreasing profit with higher rates of energy expenditure
(Ydenberg and Hurd, 1998). In the next section we show how
R can be calculated for different modes of flight, using
measured values for ground and air speed.
Combining measurements and models
Eqn·1–4 are solved using measured combinations of ground
speed and air speed, using the following parameter values to
Results
Predictions from optimal foraging and aerodynamic theory
By applying Eqn·1–4 and the biometric characteristics
(Table·2) of each gull species, to a range of air speed (Va) and
ground speed (Vg) from 5 to 25·m·s–1, we obtained patterns of
net rate of energy gain (R) for flapping and soaring flight
calculated for equal Va and Vg (Fig.·2). The assumption of
equal Va and Vg is not required in our analysis but made here
just to enable a two-dimensional graphical display of model
sensitivities. If a bird flies at the same air and ground speed
regardless of flight strategy, the net rate of energy gain is
always higher for soaring flight than for flapping flight.
Nevertheless there is a range of flight speeds where R is
similar for both flight strategies; this range is wider for blackheaded gulls than it is for lesser black-backed gulls. This is
due to the much higher cost of flapping compared to soaring
flight in lesser black-backed gulls. The difference in R
between the two flight strategies increases with very low and
very high air and ground speed combinations. However, the
decrease in R is much steeper for flapping flight at low speeds
than at high speeds and may even result in energy loss. This
implies that selecting flapping flight at the lower flight speeds
is much more costly during foraging than at higher flight
speeds.
The shape of R, as a function of Va and Vg, changes with
different combinations of En and D (Fig.·2). For example, if
flight distance (D) decreases, the range of air and ground speeds
where flapping and soaring flight result in similar net rate of
energy gain increases (Fig.·2C,D). Furthermore, the net energy
gain at lower air speeds increases. If however, the energy gain
function (En) decreases, R decreases (Fig.·2E,F). The
sensitivity of R to changes in tp is not evaluated, because the
parameter is confounded with En and D.
The relationship between Va and Vg and net energy gain is
nonlinear. By plotting the net rate of energy gain or the
difference in net energy gain between soaring and flapping
flight (Rs–Rf) in relation to multiple combinations of Va and Vg,
we can visually compare the result of different flight speed
combinations (Fig.·3). If gulls maximize their net energy gain
during foraging flights, then combinations of high Va and low
Vg as well as low Vg and high Va are not expected, especially
THE JOURNAL OF EXPERIMENTAL BIOLOGY
Energetics and flight strategy selection 3493
Black-headed gull
20
Lesser black-backed gull
B
A
10
0
Soar
Flap
Net energy gain (W)
–10
20
En = 20
D = 10000
D
C
10
0
En = 20
D = 5000
–10
20
F
E
10
0
En = 10
D = 10000
–10
0
5
10
15
20
25 0
5
Ground speed and air speed (m
10
15
20
25
s–1)
Fig.·2. Net rate of energy gain (W) of black-headed gulls (A,C,E) and lesser black-backed gulls (B,D,F) for flapping (broken line) and soaring
flight (solid line) solved for equal air speeds and ground speeds (m s–1). (A,B) En=20·W, D=10000·m, (C,D) En=20·W, D=5000·m, (E,F) En=10·W,
D=10000·m. tp is kept constant because its effect on R is inverse to that on D.
Rflap (W)
A
Black-headed gull
15
10
5
0
10
0
−10
20
10
10
20
20
Rsoar (W)
10
10
10
10
20
Vg (m s–1)
10
20
D
C
15
10
5
0
10
0
−10
20
10
Rsoar −Rflap (W)
Lesser black-backed gull
B
10
20
20
20
F
E
20
20
10
10
0
0
10
20
Vg (m s–1)
10
20
Va (m s–1)
10
20
Va (m s–1)
Fig.·3. The net energy gain (W) for flapping (Rflap; A,B) and soaring (Rsoar; C,D) and the difference between Rsoar and Rflap (E,F) considering
different combinations of air speed (Va·m·s–1) and ground speed (Vg·m·s–1) for black-headed gulls (A,C,E) and lesser black-backed gulls (B,D,F).
Parameter estimates for calculating R are as follows: En=20·W, D=10000·m, tp=1800·s.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
3494 J. Shamoun-Baranes and E. van Loon
Flight strategy
The ratio of the number of soaring and gliding to flapping
flight tracks was 2.56 in lesser-blacked backed gulls compared
to 0.75 in black-headed gulls. The frequency of soaring flight
Table·3. Air speeds, ground speeds and flight altitudes for
flapping and soaring/gliding flight of black-headed gulls and
lesser black-backed gulls
Black-headed gull
Lesser
black-backed gull
Va (m·s )
Flapping
Soaring
14.13±3.18
13.64±4.42
14.54±2.63
13.90±4.37
Vg (m·s–1)
Flapping
Soaring
14.67±3.94
14.13±4.27
13.64±3.49
15.50±4.96
Flight altitude (m)
Flapping
Soaring
Maximum
132.0±71.3
225.8±139.4
574.6
174.8±138.5
298.3±152.85
737.7
28
21
27
69
–1
Rel. freqency
Black-headed gull
and , soar
and , flap
Ground speed
20
15
10
5
0
0
0.6
0.4
0.2
0
30
5
10
B
15
20
Air speed
25
30 0 0.2 0.4 0.6
Rel. freqency
Lesser black-backed gull
and , soar
and , flap
25
20
15
10
5
N
Flapping
Soaring
A
0.6
0.4
0.2
0
30
25
Rel. freqency
Measured flight speed, direction and altitude
The mean measured Va, Vg and flight altitude of both gull
species are summarized in Table·3. The mean air speeds of
black-headed gulls and lesser black-backed gulls, regardless of
flight strategy, were higher then the predicted minimum power
speed Vmp (9.57·m·s–1 and 10.96·m·s–1, respectively) and lower
than the predicted maximum range speed Vmr (15.7 and
17.8·m·s–1, respectively). Vmp and Vmr were calculated on the
basis of the data in Table·2 (see Appendix in supplementary
material). For both species, Vg of both flight strategies
combined was positively and significantly related to Va (blackheaded gull: Vg=1.66.Va+0.93, r2=0.74, P<0.001, Fig.·4A;
lesser black-backed gull: Vg=1.61.Va+0.95, r2=0.65, P<0.001,
Fig.·4B). Flight directions (air and ground) for both species and
both flight strategies did not differ significantly from a uniform
distribution (Raleigh Test of uniformity). In this study, the
maximum flight altitude of both gull species did not exceed
1000·m (Table·3) (for more details, see Shamoun-Baranes et
al., 2006).
was higher than flapping flight at lower air speeds for both
species (Fig.·4). For both species and flight strategies the
observations were normally distributed over ground and air
speeds, on the basis of a Lilliefors test (Lilliefors, 1967). The
soaring to flapping ratio increased at higher winds speeds in
both species. For wind speeds ⭐5·m·s–1, the soar/flap ratio was
0.52 for black-headed gulls and 2 for lesser black-backed gulls.
For winds speeds >5·m·s–1, the ratio was 7 for black-headed
gulls and 3.88 for lesser black-backed gulls. There were no
tracks of black-headed gulls at wind speeds above 7.0·m·s–1
whereas lesser black-backed gulls were recorded in soaring
flight at a maximum wind speed of 11·m·s–1.
Ground speed
during flapping flight. The power needed for flight varies with
Va in flapping flight, but is constant in soaring flight. Therefore,
increased ground speeds results in higher net energy gain
regardless of air speeds during soaring. The difference in R
between flight strategies is highest for combinations of high Va
and low Vg and low Va and Vg and is much higher in lesser
black-backed gulls than in black-headed gulls.
Va, air speed; Vg, ground speed.
Values are means and s.d. (N=number of observations).
0
0
5
10
15
20
Air speed
25
30 0 0.2 0.4 0.6
Rel. freqency
Fig.·4. Observed ground speeds (Vg, m·s–1) and observed air speeds
(Va, m·s–1) during flapping (+) and soaring (䊊) flight for the blackheaded gull (A) and the lesser black-backed gull (B). Regression lines
are shown for each species and each flight strategy (solid line for
soaring, broken line for flapping; the 1:1 line is included for reference
purposes). The frequency distributions of Va and Vg during soaring and
flapping flight are presented along the respective axes at the right and
top. The lines of the frequency distributions are shifted slightly along
the category axis for display purposes.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
Energetics and flight strategy selection 3495
A
15
A
10
Net energy gain (R, in W)
5
Black-headed gull
B
15
10
5
Lesser black-backed gull
0
5
10
15
20
Difference in net energy gain (W)
8
10
Black-headed gull
6
4
2
B
10
Lesser black-backed gull
8
6
4
2
25
2
4
6
8
10 12 14 16 18 20 22 24 26
Va (m s–1)
Va (m s–1)
Combining measurements and models
Eqn.·1–4 were solved using measured Va and Vg and observed
flight strategy. The predicted values for R solved for the observed
combination of flight strategy, Va and Vg, were always positive
and within the same range of values for both species (Fig.·5).
The predicted difference in net energy gain between soaring and
flapping (Rs–Rf) was also calculated with measured Va and Vg
combinations. For black-headed gulls, only small differences in
R (⭐3·W) were predicted between flight strategies for the
observed combinations of air speeds and ground speeds (Fig.·6).
These values were slightly higher for lesser black-backed gulls.
In all cases where the predicted Rs–Rf was greater than 6·W,
lesser black-backed gulls were observed soaring.
For descriptive purposes, the distribution of measured Va for
each flight strategy is summarized with the normal probability
distribution function (Fig.·7). This simplification of the data
helps to clarify patterns in the data and can be used to predict
the ratio between soaring and flapping flight, while providing
an excellent fit with the measurements. For example, from the
observations, we find that flapping flight is more predominant
than soaring flight for Va between 10.8 and 18.5·m·s–1 for
black-headed gulls and Va between 11.6 and 18.3·m·s–1 for
lesser black-backed gulls. We can also derive a range of values
where the soar/flap ratio is <1 by applying other selection
criteria, for example, based on calculated R for each flight
strategy and the difference in R between flight strategies
(Rs–Rf). The predicted ratios of soaring to flapping in relation
to Va (calculated using the normal probability distributions
shown in Fig.·7) are not significantly different from the
observed ratio of soaring/flapping flight based on the Va range
Fig.·6. The predicted differences in net energy gain between soaring
(䊊) and flapping (+) flight (Rs–Rf) calculated with observed
combinations of air speed (Va, m·s–1), ground speed (Vg, m·s–1) and
flight strategy, for black-headed gulls (A) and lesser black-backed
gulls (B). Parameter estimates for R are as follows: En=20·W,
D=10000·m, tp=1800·s. As observed Va and Vg values are highly
confounded (as shown in Fig.·4), Rs–Rf was not plotted against both
Va and Vg.
0.2
A
Rdiff<1.9
Rs>11.6
Rf>10.3
0.15
Normalised probability
Fig.·5. The predicted net energy gain (R, in W) calculated with
observed ground speed (Vg), air speed (Va) and flight strategy
combinations. Different symbols (+ flapping, 䊊 soaring) represent the
predicted R for a measured combination of Vg, Va and flight strategy
for black-headed gulls (A) and lesser black-backed gulls (B).
Parameter estimates for R calculations are as follows: En=20·W,
D=10000·m, tp=1800·s. As observed Va and Vg values are highly
confounded (see Fig. 4), R was not plotted against both Va and Vg.
0.1
Black-headed gull
0.05
0.2
B
Rdiff<4.9
Rs>10.4
Rs>5.7
0.15
Lesser black-backed gull
0.1
0.05
0
5
10
15
Va (m s–1)
20
25
30
Fig.·7. The estimated normal probability distribution of air speeds (Va,
m·s–1) during flapping (broken line) and soaring flight (solid line) for
the black-headed gulls (A) and lesser black-backed gulls (B). This
simplification of the data can be used to predict, for example, the range
of Va where the soar/flap ratio is <1 (area shaded in gray). Thick solid
lines at the top of each figure represent alternative selection criteria
that can be used to determine the soar/flap ratio <1 (area shaded in
gray): for example, where Rdiff<1.9·W and Rs>11.6 (black-headed
gull).
mentioned above (based on a Chi-square test, ␹2=0.06, P=0.99,
d.f.=3). The observed and predicted ratios of soaring to flapping
are given in Table·4.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
3496 J. Shamoun-Baranes and E. van Loon
Table·4. Observed and predicted soar/flap ratio for blackheaded gulls and lesser black-backed gulls
Black-headed gull
Lesser
black-backed gull
Observed
>1
<1
1.63
0.83
1.79
0.72
Predicted
>1
<1
1.69
0.78
2.13
0.69
Soar/Flap ratio
The criteria for calculating soar/flap ratio <1 for black-headed
gulls are 10.8⭓Va⭐18.5·m·s–1 and 11.6⭓Va⭐18.3·m·s–1 for lesser
black-backed gulls. The soar/flap ratio <1 was predicted by
calculating the ratio of surface of soaring to the surface of flapping
within the corresponding gray areas (see Fig.·7) for each species. The
observed values do not differ significantly from the predicted ones
according to a Chi-square test (␹2=0.06, P=0.99).
Discussion
This study outlines a model that brings together foraging,
flight energetics and flight behavior theories. The observations
made during this study strongly support the hypothesis that
flight and foraging energetics influence the selection of flight
strategy during travel between foraging sites. When testing
different combinations of air speed and ground speed and
different parameter sets, we arrive at a theoretical range of flight
speeds where soaring is much more beneficial than flapping.
Furthermore, with our model framework we reach a high degree
of explanation of the observed ratio of soaring to flapping flight.
In accordance with theoretical predictions, when the predicted
net rate of energy gain for soaring is much larger than for
flapping, a bird selects soaring flight. This is also reflected in
the higher proportion of soaring flight for lesser black-backed
gulls than black-headed gulls. Combinations of ground and air
speed that would result in very low or negative net rate of energy
gain were not found within the tested parameter space. Although
energetics is a proximate mechanism influencing the selection
of flight strategy other factors may also be influential.
One main factor that may influence flight strategy is the
weather. Meteorological conditions may not only influence the
ability of a bird to flap or soar but also the energy expenditure
or time needed for flight. Although the relationship between
meteorological conditions and gull flight strategy is the focus
of a different study (E. van Loon and J. Shamoun-Baranes,
manuscript in preparation), we briefly discuss the potential
influence of meteorological conditions on flight strategy
selection. The soaring flight behavior of raptors, storks and
pelicans is strongly influenced by characteristics of the
convective boundary layer (Kerlinger, 1989; Shannon et al.,
2002; Shamoun-Baranes et al., 2003). Several studies have
found a relationship between the flight strategy selection
of different avian species and meteorological conditions
(Woodcock, 1940a; Woodcock, 1940b; Woodcock, 1975;
Bruderer et al., 1994; Spaar et al., 1998; Sergio, 2003).
In our study, it is clear that wind speed and direction can
strongly influence both the time and energy budget of a bird
and hence the net rate of energy gain. If a bird attempts to
maximize the net rate of energy gain then both the travel time
(inversely related to ground speed) and cost of flight (a function
of air speed) should be minimized. How a bird responds to wind
can influence its ground speed (and hence travel time) as well
as its air speed (influencing the cost of flight) and also,
therefore, its flight strategy selection. As found in this study
and several others (Pennycuick, 1982; Flint and Nagy, 1984;
Rosen and Hedenström, 2001), the proportion of soaring flight
increases with increasing wind speeds. Given the spatial and
temporal resolution of our data and our model framework,
however, like others, we cannot explain this relationship. Gulls
over the sea used three predominant forms of flight: (1)
flapping, (2) convective soaring (circling in thermal updrafts)
and (3) linear soaring (soaring into the wind and increasing
flight altitude) (Woodcock, 1940a; Woodcock, 1940b;
Woodcock, 1975). These flight strategies were clearly
associated with certain sea–air temperature and wind speed
conditions. Perhaps the increasing proportion of soaring flight
with increased wind speed is related to the flexibility of gulls
to exploit a wide range of wind speeds by using different
soaring techniques, as observed by Woodcock (Woodcock,
1940a; Woodcock, 1940b; Woodcock, 1975).
The relationship between flight strategy, energetics during
foraging and weather will be influenced by the spatial foraging
behavior of gulls. When gulls randomly search for food,
soaring and flapping flight will occur in similar ratios for
different wind directions. Alternatively, wind speeds and
directions will have a strong influence on time and energy and
hence flight strategy when there is a preference for a food
source at a specific spatial location. A difference in flapping to
soaring ratios for different flight directions would suggest the
existence of a preferred feeding location. Several studies have
shown that gull species such as black-headed gulls, lesser
black-backed gulls and herring gulls show foraging site fidelity
or predictable foraging movements (Morris and Black, 1980;
Horton et al., 1983; Gorke and Brandl, 1986; Prevot-Julliard
and Lebreton, 1999). If birds do not have a preferential
direction when foraging than we may expect them to select
flight directions in relation to wind. Soaring albatrosses (Order
Procellariformes) preferred foraging flight directions according
to wind directions and achieved higher ground speeds in tail
and side winds, reducing the cost of soaring flight
(Weimerskirch et al., 2000).
The cost of flight is an additional factor influencing our
parameter space. If, for example, the energetic cost of flapping
flight is lower than presently calculated, the range of overlap
where both flight strategies will have a similar energetic
benefit will increase. The predictions in this study assume
constant flapping vs constant soaring. However, gulls often use
a mixture of flap-gliding and appear to be quite flexible in their
flight strategy selection. The heart rate of herring gulls during
flapping flight was highly variable (Kanwisher et al., 1978)
and may be due to this flexibility in flap-gliding strategy. By
THE JOURNAL OF EXPERIMENTAL BIOLOGY
Energetics and flight strategy selection 3497
efficiently using different flight strategies gulls may take
advantage of a wide range of air movements and quasi twodimensional structures in the atmospheric boundary layer
(Young et al., 2002). The cost of flight is determined in this
study by Eqn·3 and 4. The accuracy and precision of these
equations depends on model input as well as parameter
uncertainty. Sometimes these two sources of uncertainty
interact in a complex way. For example, air density will
influence the cost of flight at a given air speed. In our
calculations air density is set to 1.225·kg·m–3. This is the air
density according to the properties of the Standard
Atmosphere at sea level with a barometric pressure of
1013.25·mb and a temperature 15°C in dry air (US Standard
Atmosphere, 1976). However, air density is influenced by
barometric pressure, temperature and the amount of water
vapor in the air (Holton, 2004). Considering Standard
Atmosphere properties, density decreases with altitude (at
1000·m, air density=1.11·kg·m–3) resulting in decreasing
parasite power and increasing induced power. In this case,
observations of barometric pressure with altitude as well
as parameter estimates in the parasite power and induced
power equations are interacting. An example of parameter
uncertainty is the conversion of mechanical power to
metabolic power output during flight. In this study we apply a
constant conversion efficiency of 0.23 for both species;
however, a flight muscle efficiency of 0.18 was found to be
more accurate for birds the size of a starling (Sturnus vulgaris)
weighing approximately 100·g (Ward et al., 2001).
Alternatively, the conversion efficiency may scale with mass
(Bishop, 2005). Other factors such as the body drag coefficient
(Hedenström and Liechti, 2001; Maybury and Rayner, 2001)
or the shape of the power curve itself (Dial et al., 1997;
Rayner, 2001; Tobalske et al., 2003) are still being debated in
the literature. In order to appropriately determine the
sensitivity of our model to different inputs and parameter
settings in Eqn·2, 3 and 4 a full sensitivity analysis, as was
conducted by Spedding and Pennycuick for the flight power
curve (Spedding and Pennycuick, 2001), is needed. This is
beyond the scope of this paper but will be a topic of future
research.
On the basis of our study we may articulate some new,
testable, hypotheses about flight strategy during foraging. Gulls
may show a higher tendency for flapping flight (1) when
soaring is not possible or less efficient than flapping (for
example due to meteorological conditions); (2) when flapping
is possible at the range of flight speeds where the difference
between soaring and flapping net energy gain is minimal and
the net energy gain is above a certain critical value. As a
function of patch quality, average flight distance to patches and
average feeding duration, gulls will change the ratio of soaring
to flapping flight. With increasing foraging distances, the range
of flight speeds where net energy gain is similar between flight
strategies decreases. Black-headed gulls (Gorke and Brandl,
1986; Prevot-Julliard and Lebreton, 1999) and herring gulls
(Belant et al., 1993) showed increasing foraging distances later
in the breeding period. It can therefore be expected that gulls
will show a higher proportion of soaring flight later in the
breeding season as foraging distances increase and the
difference in net energy gain during soaring and flapping
increases.
The fit between our model and the observations is very
close. Considering the spatial and temporal scale of our
measurements, and the lack of exact information on gull
activity, we think that more extensive models would not
increase our insight. We expect that a high resolution,
homogeneous dataset for both weather and flight speeds over
longer periods of time, accompanied by time budget
information for the birds, would not only improve the fit
between our current model and measurements but also, and
more importantly, further our understanding of the factors
influencing flight strategy selection.
We especially thank Hans van Gasteren and Jelmer van
Belle, Royal Netherlands Air Force, for their fieldwork in
collecting and processing the gull tracking data. We also thank
Willem Bouten and two anonymous reviewers for their
constructive comments on a previous version of this
manuscript. Meteorological data were provided by the Royal
Netherlands Meteorological Institute (KNMI) and we thank H.
Klein Baltink for his feedback. This study was conducted
within the Virtual Laboratory for e-Science project (www.vle.nl), supported by a BSIK grant from the Dutch ministry of
education, culture and science and the ICT innovation
program of the ministry of economic affairs.
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